
"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  [
	    '''encoder.version''',
	    '''decoder.version''',
	    '''model.encoder.version''',
	    '''model.decoder.version''',
	    '''_float_tensor''',
	    '''decoder.output_projection.weight''',
	]
	for k in ignore_keys:
		state_dict.pop(_lowerCamelCase,					_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		Tuple			)					->						str:

	'''simple docstring'''




	lowerCAmelCase							,       lowerCAmelCase												=  emb.weight.shape
	lowerCAmelCase												=  nn.Linear(_lowerCamelCase,					_lowerCamelCase,					bias=_lowerCamelCase			)
	lowerCAmelCase												=  emb.weight.data
	return lin_layer




def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Any="facebook/mbart-large-en-ro",					_lowerCamelCase:		List[str]=False,					_lowerCamelCase:		Tuple=False			)					->						Dict:

	'''simple docstring'''




	lowerCAmelCase												=  torch.load(_lowerCamelCase,					map_location='''cpu'''			)['''model''']
	remove_ignore_keys_(_lowerCamelCase			)
	lowerCAmelCase												=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

	lowerCAmelCase												=  MBartConfig.from_pretrained(_lowerCamelCase,					vocab_size=_lowerCamelCase			)
	if mbart_aa and finetuned:
		lowerCAmelCase												=  '''relu'''

	lowerCAmelCase												=  state_dict['''decoder.embed_tokens.weight''']
	lowerCAmelCase												=  MBartForConditionalGeneration(_lowerCamelCase			)
	model.model.load_state_dict(_lowerCamelCase			)

	if finetuned:
		lowerCAmelCase												=  make_linear_from_emb(model.model.shared			)

	return model


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
					)
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument(
					    """--hf_config""",
					    default="""facebook/mbart-large-cc25""",
					    type=str,
					    help="""Which huggingface architecture to use: mbart-large""",
					)
					parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
					parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
					UpperCAmelCase        =				parser.parse_args()
					UpperCAmelCase        =				convert_fairseq_mbart_checkpoint_from_disk(
					    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
					)
					model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""






def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Any			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  [1]
	for i in range(2,					_lowerCamelCase			):
		factorials.append(factorials[-1] * i			)
	assert 0 <= k < factorials[-1] * n, "k out of bounds"

	lowerCAmelCase												=  []
	lowerCAmelCase												=  list(range(_lowerCamelCase			)			)

	# Find permutation
	while factorials:
		lowerCAmelCase												=  factorials.pop()
		lowerCAmelCase							,       lowerCAmelCase												=  divmod(_lowerCamelCase,					_lowerCamelCase			)
		permutation.append(elements[number]			)
		elements.remove(elements[number]			)
	permutation.append(elements[0]			)

	return permutation


if __name__ == "__main__":
					import doctest

					doctest.testmod()


